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. 2024 Oct 10:13:1035.
doi: 10.12688/f1000research.154680.2. eCollection 2024.

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine

Ali Muhaimil  1 , Saikiran Pendem  1 , Niranjana Sampathilla  2 , Priya P S  3 , Kaushik Nayak  1 , Krishnaraj Chadaga  4 , Anushree Goswami  2 , Obhuli Chandran M  1 , Abhijit Shirlal  1
Affiliations

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine

Ali Muhaimil et al. F1000Res. .

Abstract

Background: Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine.

Methods: This was a prospective case-control study. A total of 200 MRI patients (100 cases and controls each) referred for lumbar spine and whole spine screening were included. The scans were performed using 3.0 Tesla MRI (United Imaging Healthcare). T2 weighted images of the lumbar spine were segmented to extract radiomic features. Machine learning (ML) models, such as random forest, decision tree, logistic regression, K-nearest neighbors, adaboost, and deep learning methods (DL), such as ResNet and GoogleNet, were used, and performance measures were calculated.

Results: Our study showed that Random forest and AdaBoost are the most reliable ML models for predicting LBP. Random forest showed high performance with area under curve (AUC) values from 0.83 to 0.88 across all lumbar vertebrae and L2-L3, L3-L4, and L4-L5 intervertebral discs (IVDs), with AUCs of 0.88 the highest at L5-S1 IVD (0.92). Adaboost demonstrated high performance at the L2-L5 vertebrae with AUC values of 0.82 to 0.90, with the highest AUC (0.97) at the L5-S1 IVD. Among the DL models, GoogleNet outperformed the other models at 30 epochs with an accuracy of 0.85, followed by ResNet 18 (30 epochs) with an accuracy of 0.84.

Conclusion: The study demonstrated that ML and DL models can effectively predict LBP from MRI T2 weighted image of the lumbar spine. ML and DL models could also enhance the diagnostic accuracy of LBP, potentially leading to better patient management and outcomes.

Keywords: Deep learning; Machine learning; intervertebral discs; low back pain; lumbar vertebrae.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Showing the segmentation of lumbar vertebrae and intervertebral disc on T2 weighted image.
Figure 2.
Figure 2.. Architectural configuration delineating the structure of ResNet50.
Figure 3.
Figure 3.. Architectural configuration delineating the structure of ResNet18.
Figure 4.
Figure 4.. Architectural configuration delineating the structure of GoogleNet.
Figure 5.
Figure 5.. ROC curve and confusion matrix for random forest (a,b) and adaboost (c,d) at L4.
Figure 6.
Figure 6.. ROC curve and confusion matrix for random forest (a,b) and adaboost (c,d) at L5.
Figure 7.
Figure 7.. ROC curve and confusion matrix for random forest (a,b) and adaboost (c,d) at L5-S1 IVD.
See this image and copyright information in PMC

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